Evaluating CNVII Recovery after Reconstruction with Vascularized Nerve Grafts: A Retrospective Case Series
Bibliographic record
Abstract
Summary: Few studies have evaluated vascularized nerve grafts (VNGs) for facial nerve (CNVII) reconstruction. We sought to evaluate long-term outcomes for CNVII recovery following reconstruction with VNGs. A retrospective review of all patients at a tertiary centre who underwent radical parotidectomy and immediate CNVII reconstruction with VNGs was performed (January 2009–December 2019). Preoperative demographics, perioperative factors (flap type, source of VNGs), and postoperative factors [complications, adjuvant therapy, revisionary procedures, length of follow-up, and CNVII function via the House-Brackmann scale (HB)] were collected. Data were summarized qualitatively. Twelve patients (Mage= 53 ± 18 years) with a mean follow-up of 33 (± 23) months were included. Six patients underwent reconstruction with a radial forearm flap and dorsal sensory branches of the radial nerve. Six patients underwent reconstruction with an anterolateral thigh flap and only deep motor branches of the femoral nerve to the vastus lateralis (n = 4) or combined with the lateral femoral cutaneous nerve (n = 2). Two patients regained nearly normal function (HB = 2). Eight patients regained at least resting symmetry (HB = 3 for n = 7; HB = 4 for n = 1). One patient regained a flicker of movement (HB = 5). One patient did not regain function (HB = 6). Six patients had static revision procedures to improve symmetry. Five patients had disease recurrence; 3 died from their disease. VNGs offer a practical and viable addition to the CNVII reconstruction strategy, and result in good functional recovery with acceptable donor site deficits. The associated adipofascial component of these flaps can also augment the soft tissue defect left after tumor ablation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".